Contribution of Geographical Information Systems to the Management of Volcanic Crises

The incorporation of a set ofcomputer-based tools, such as Geographical InformationSystems and physical models, to the field of riskassessment, introduces a new perspective in thevolcanic risk maps production, increasing the analysisand modelling capabilities available through theapplication of conventional methodologies. Amethodology adapted to the requirements andcharacteristics of the new operating environment hasbeen applied at Tenerife island (Canary Islands,Spain) to carry out a study devoted to analyse thesuitability of these tools for near real-timemanagement of volcanic crises. With this in mind, aseries of potential eruption scenarios have beenselected to identify and characterise which elementsat risk would prove most vulnerable against a specificvolcanic phenomenon, depending on the socio-economiccharacteristics of the area affected and the resultingdistribution of the volcanic products. This kind ofinformation is fundamental to update, adapt or produceeffective risk management and emergency plans orprotocols, where the measures to mitigate or fightagainst a specific volcanic disaster have to be taken,incorporating the existing knowledge of the phenomenonbehaviour and taking into account their potentialeffects on the area of interest.     

Cladoceran and chironomid assemblages as qualitative indicators of water depth in subarctic Fennoscandian lakes

The relationship between surface-sediment cladoceran and chironomid communities to lake depth was analysed in 53 lakes distributed across timberline in northern Fennoscandia using multivariate statistical approaches. The study sites are small and bathymerically simple, with water depth ranging from 0.85-27.0 m (mean 6.36 m). Maximum lake depth was the most important factor in explaining the cladoceran distributions and the second most important factor in explaining the chironomid distributions in these subarctic lakes, as assessed on the basis of a series of constrained RDAs, Monte Carlo permutation tests, and variance partitioning. Quantitative inference models for maximum lake depth were created for both groups of animals. Well-performing calibration functions for predicting lake depth were obtained in each case using linear partial least squares (PLS) regression and calibration, weighted averaging (WA) with an 'inverse' deshrinking regression, and weighted averaging partial least squares (WA-PLS). Quantitative reconstructions of lake level fluctuations should be possible from cladoceran and chironomid core data with a root mean squared error of prediction (RMSEP), as estimated by jack-knifing, of about 1.6-3.0 m.     

典型红壤丘陵区土地利用空间优化配置

选择具有红壤丘陵区典型特征的江西省泰和县千烟洲为研究区域,在分析当前土地利用结构并进行土地适宜性评价的基础上,依据2000年千烟洲试验站研究数据及农户调查资料,将GIS技术与线性规划模型有效地耦合起来,探讨实现典型红壤丘陵区各种具有特定数量、质量的农用土地资源在空间上最优配置的方法与技术。结果表明,优化方案从现状中牧业短缺的粮、果、林结构转化为较为完善的粮、果、牧、林结构,耕地资源利用结构也由重水轻旱、粮作为主、经作为辅的传统生产模式,转变为粮-经-饲并举的三元优化配置结构;同时,由果业和畜牧业共同支撑的优化方案的经济效益明显优于现状,在经济上更具稳定性和可持续性。     

Assessment of climate change impacts on the hydrology of the Peruvian Amazon–Andes basin

In this article, we propose an investigation of the modifications of the hydrological response of two Peruvian Amazonas–Andes basins in relationship with the modifications of the precipitation and evapotranspiration rates inferred by the IPCC. These two basins integrate around 10% of the total area of the Amazonian basin. These estimations are based on the application of two monthly hydrological models, GR2M and MWB3, and the climatic projections come from BCM2, CSMK3 and MIHR models for A1B and B1 emission scenarios (SCE A1B and SCE B1). Projections are approximated by two simple scenarios (anomalies and horizon) and annual rainfall rates, evapotranspiration rates and discharge were estimated for the 2020s (2008–2040), 2050s (2041–2070) and 2080s (2071–2099). Annual discharge shows increasing trend over Requena basin (Ucayali river), Puerto Inca basin (Pachitea river), Tambo basin (Tambo river) and Mejorada basin (Mantaro river) while discharge shows decreasing trend over the Chazuta basin (Huallaga river), the Maldonadillo basin (Urubamba river) and the Pisac basin (Vilcanota river). Monthly discharge at the outlet of Puerto Inca, Tambo and Mejorada basins shows increasing trends for all seasons. Trends to decrease are estimated in autumn discharge over the Requena basin and spring discharge over Pisac basin as well as summer and autumn discharges over both the Chazuta and the Maldonadillo basins. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparative probabilistic assessment of the hydrological performance of reconstructed and natural watersheds

The oil sands industry has committed to returning the mine sites to a productive condition. The reconstructed soil covers must have sufficient available water holding capacity (AWHC) to supply enough moisture over the growing season, to promote vegetation. In order to assess the sustainability of various soil cover alternatives, a generic, system dynamic watershed model entitled GSDW was used along with the available historical meteorological records to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted; consequently, frequency curves of the maximum annual moisture deficit values are constructed and used to assess the probability that various reconstructed and natural watersheds can provide the associated moisture demands. In general, the study showed a tendency for the reconstructed watershed to provide less moisture for evapotranspiration than natural systems. Watersheds of various soil types, layering, thicknesses and topography were studied. The gained knowledge was used to predict the possible performance of a hypothetical reclamation cover. The results indicated that the hypothetical cover performed in a similar manner to the thickest existing soil cover which confirmed a high probability of that cover to survive under the same existing climatic conditions. Moreover, this probabilistic framework was found to be useful for integrating information gained from natural watersheds (e.g. the canopy of mature natural systems and transfer the results to the reconstructed system). The results show that the canopy influenced the moisture deficit regime positively which signifies a greater possibility that reconstructed covers will adapt to vegetation type. In brief, the adopted approach enables better understanding of the response of reconstructed systems via multiple simulations of ‘what‐if’ scenarios using different soil/vegetation alternatives. Copyright © 2010 John Wiley & Sons, Ltd.     

Relationship between South China Sea precipitation variability and tropical Indo-Pacific SST anomalies in IPCC CMIP5 models during spring-to-summer transition

The present study evaluates the precipitation variability over the South China Sea (SCS) and its relationship to tropical Indo-Pacific SST anomalies during spring-to-summer transition (April-May-June, AMJ) simulated by 23 Intergovernmental Panel on Climate Change Coupled Model Intercomparison Project Phase 5 coupled models. Most of the models have the capacity to capture the AMJ precipitation variability in the SCS. The precipitation and SST anomaly (SSTA) distribution in the SCS, tropical Pacific Ocean (TPO), and tropical Indian Ocean (TIO) domains is evaluated based on the pattern correlation coefficients between model simulations and observations. The analysis leads to several points of note. First, the performance of the SCS precipitation anomaly pattern in AMJ is model dependent. Second, the SSTA pattern in the TPO and TIO is important for capturing the AMJ SCS precipitation variability. Third, a realistic simulation of the western equatorial Pacific (WEP) and local SST impacts is necessary for reproducing the AMJ SCS precipitation variability in some models. Fourth, the overly strong WEP SST impacts may disrupt the relationship between the SCS precipitation and the TPO-TIO SST. Further work remains to be conducted to unravel the specific reasons for the discrepancies between models and observations in various aspects.     

Performance of Convective Parameterization Schemes in Asia Using RegCM:Simulations in Three Typical Regions for the Period 1998–2002

This study discusses the sensitivity of convective parameterization schemes(CPSs) in the Regional Climate Model(version 4.3)(Reg CM4.3) over East/South Asia. The simulations using different CPSs in Reg CM are compared to discover a suitable scheme for this region, as the performance of different schemes is greatly influenced by region and seasonality. Over Southeast China and the Bay of Bengal, the Grell scheme exhibits the lowest RMSEs of summer precipitation compared to observed data. Moreover, the Emanuel over land and Grell over ocean(ELGO) scheme enhances the simulation, in comparison with any single CPS(Grell/Emanuel) over Western Ghats, Sri Lanka, and Southeast India. Over the Huang–Huai–Hai Plain(3H) and Tibetan Plateau(TP) regions of China, the Tiedtke scheme simulates the more reasonable summer precipitation with high correlation coefficient and comparable amplitude. Especially, it reproduces a minimum convective precipitation bias of 8 mm d-1and the lowest RMSEs throughout the year over East/South Asia. Furthermore, for seasonal variation of precipitation, the Tiedtke scheme results are closer to the observed data over the 3H and TP regions. However, none of the CPSs is able to simulate the seasonal variation over North Pakistan(NP). In comparison with previous research, the results of this study support the Grell scheme over South Asia. However, the Tiedtke scheme shows superiority for the 3H, TP and NP regions. The thicker PBL, less surface latent heat flux, the unique ability of deep convection and the entrainment process in the Tiedtke scheme are responsible for reducing the wet bias.     

Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain)

Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.     

Spatial structure analysis of a reptile community with airborne LiDAR data

The analysis of the spatial structure of animal communities requires spatial data to determine the distribution of individuals and their limiting factors. New technologies like very precise GPS as well as satellite imagery and aerial photographs of very high spatial resolution are now available. Data from airborne LiDAR (Light Detection and Ranging) sensors can provide digital models of ground and vegetation surfaces with pixel sizes of less than 1 m. We present the first study in terrestrial herpetology using LiDAR data. We aim to identify the spatial patterns of a community of four species of lizards (Lacerta schreiberi, Timon lepidus, Podarcis bocagei, and P. hispanica), and to determine how the habitat is influencing the distribution of the species spatially. The study area is located in Northern Portugal. The position of each lizard was recorded during 16 surveys of 1 h with a very precise GPS (error < 1 m). LiDAR data provided digital models of surface, terrain, and normalised height. From these data, we derived slope, ruggedness, orientation, and hill-shading variables. We applied spatial statistics to determine the spatial structure of the community. We computed Maxent ecological niche models to determine the importance of environmental variables. The community and its species presented a clustered distribution. We identified 14 clusters, composed of 1–3 species. Species records showed two distribution patterns, with clusters associated with steep and flat areas. Cluster outliers had the same patterns. Juveniles and subadults were associated with areas of low quality, while sexes used space in similar ways. Maxent models identified suitable habitats across the study area for two species and in the flat areas for the other two species. LiDAR allowed us to understand the local distributions of a lizard community. Remotely sensed data and LiDAR are giving new insights into the study of species ecology. Images of higher spatial resolutions are necessary to map important factors such as refuges.